beta_q0n <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
select_if(~!all(. == 0)) %>%
hillpair(., q = 0)
beta_q1n <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
select_if(~!all(. == 0)) %>%
hillpair(., q = 1)
beta_q1p <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
select_if(~!all(. == 0)) %>%
hillpair(., q = 1, tree = genome_tree)
beta_q1f <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
select_if(~!all(. == 0)) %>%
hillpair(., q = 1, dist = dist)
Permanova
#Richness
betadisper(beta_q0n$C, sample_metadata$region) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.063118 0.063118 11.828 999 0.001 ***
Residuals 56 0.298846 0.005337
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Daneborg Ittoqqortoormii
Daneborg 0.001
Ittoqqortoormii 0.0011087
adonis2(beta_q0n$C ~ region,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1n$C))),
permutations = 999) %>%
broom::tidy() %>%
tt()
| term |
df |
SumOfSqs |
R2 |
statistic |
p.value |
| region |
1 |
1.037174 |
0.2581367 |
19.4856 |
0.001 |
| Residual |
56 |
2.980753 |
0.7418633 |
NA |
NA |
| Total |
57 |
4.017927 |
1.0000000 |
NA |
NA |
#Neutral diversity
betadisper(beta_q1n$C, sample_metadata$region) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.47943 0.47943 32.667 999 0.001 ***
Residuals 56 0.82185 0.01468
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Daneborg Ittoqqortoormii
Daneborg 0.001
Ittoqqortoormii 4.391e-07
adonis2(beta_q1n$C ~ region,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1n$C))),
permutations = 999) %>%
broom::tidy() %>%
tt()
| term |
df |
SumOfSqs |
R2 |
statistic |
p.value |
| region |
1 |
2.066564 |
0.2792806 |
21.70015 |
0.001 |
| Residual |
56 |
5.333033 |
0.7207194 |
NA |
NA |
| Total |
57 |
7.399597 |
1.0000000 |
NA |
NA |
#Phylogenetic diversity
betadisper(beta_q1p$C, sample_metadata$region) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.09491 0.094915 11.957 999 0.001 ***
Residuals 56 0.44451 0.007938
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Daneborg Ittoqqortoormii
Daneborg 0.002
Ittoqqortoormii 0.0010468
adonis2(beta_q1p$C ~ region,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1n$C))),
permutations = 999) %>%
broom::tidy() %>%
tt()
| term |
df |
SumOfSqs |
R2 |
statistic |
p.value |
| region |
1 |
0.09872015 |
0.09107706 |
5.611383 |
0.001 |
| Residual |
56 |
0.98519887 |
0.90892294 |
NA |
NA |
| Total |
57 |
1.08391902 |
1.00000000 |
NA |
NA |
#Functional diversity
betadisper(beta_q1f$C, sample_metadata$region) %>% permutest(., pairwise = TRUE)
adonis2(beta_q1f$C ~ region,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1n$C))),
permutations = 999) %>%
broom::tidy() %>%
tt()
Richness diversity plot
beta_q0n$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
dplyr::left_join(sample_metadata, by = join_by(sample == sample)) %>%
group_by(region) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = region, fill = region)) +
scale_color_manual(name="Region",
values=c("#6A9AC3","#F3B942")) +
scale_fill_manual(name="Region",
values=c("#6A9AC350","#F3B94250")) +
geom_point(size = 4) +
# stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9, show.legend = FALSE) +
theme_classic() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 20, face = "bold"),
axis.text = element_text(face = "bold", size = 18),
panel.background = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 18),
legend.position = "right", legend.box = "vertical"
)
Neutral diversity plot
beta_q1n$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
dplyr::left_join(sample_metadata, by = join_by(sample == sample)) %>%
group_by(region) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = region, fill = region)) +
scale_color_manual(name="Region",
values=c("#6A9AC3","#F3B942")) +
scale_fill_manual(name="Region",
values=c("#6A9AC350","#F3B94250")) +
geom_point(size = 4) +
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9) +
theme_classic() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 20, face = "bold"),
axis.text = element_text(face = "bold", size = 18),
panel.background = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 18),
legend.position = "right", legend.box = "vertical"
)
Phylogenetic diversity plot
beta_q1p$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
dplyr::left_join(sample_metadata, by = join_by(sample == sample)) %>%
group_by(region) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = region, fill = region)) +
scale_color_manual(name="Region",
values=c("#6A9AC3","#F3B942")) +
scale_fill_manual(name="Region",
values=c("#6A9AC350","#F3B94250")) +
geom_point(size = 4) +
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9) +
theme_classic() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 20, face = "bold"),
axis.text = element_text(face = "bold", size = 18),
panel.background = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 18),
legend.position = "right", legend.box = "vertical"
)
